Overview

Dataset statistics

Number of variables37
Number of observations12452
Missing cells262434
Missing cells (%)57.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory296.0 B

Variable types

Categorical5
Numeric5
DateTime6
Boolean1
Unsupported20

Warnings

ID_BATCH_PILE has a high cardinality: 12452 distinct values High cardinality
ID_BATCH_LOT has a high cardinality: 4152 distinct values High cardinality
ID_PILE has a high cardinality: 2848 distinct values High cardinality
TYPE_PILE_END has 2931 (23.5%) missing values Missing
NB_TRAY_INOCULATED has 474 (3.8%) missing values Missing
DATE_INOCULATION has 765 (6.1%) missing values Missing
DATE_END_CYCLE has 1528 (12.3%) missing values Missing
DATE_SIEVING has 2523 (20.3%) missing values Missing
DATE_END has 2848 (22.9%) missing values Missing
IS_CONFORM has 1560 (12.5%) missing values Missing
CONFORMITY_TYPE has 12452 (100.0%) missing values Missing
AGE_PILE has 765 (6.1%) missing values Missing
PILE_WEIGHT_BEGIN has 12452 (100.0%) missing values Missing
LOT_WEIGHT_BEGIN has 12452 (100.0%) missing values Missing
PILE_WEIGHT_END has 12452 (100.0%) missing values Missing
LOT_WEIGHT_END has 12452 (100.0%) missing values Missing
WEIGHT_SUBSTRACT has 12452 (100.0%) missing values Missing
QR_CODE has 12452 (100.0%) missing values Missing
QR_CODE_GZ has 12452 (100.0%) missing values Missing
AVG_TEMPERATURE has 12452 (100.0%) missing values Missing
TIME_TEMP_INF_CRI has 12452 (100.0%) missing values Missing
TIME_TEMP_INF_MIN has 12452 (100.0%) missing values Missing
TIME_TEMP_NORM has 12452 (100.0%) missing values Missing
TIME_TEMP_SUP_MIN has 12452 (100.0%) missing values Missing
TIME_TEMP_SUP_CRI has 12452 (100.0%) missing values Missing
AVG_HUMIDITY has 12452 (100.0%) missing values Missing
TIME_HUM_INF_CRI has 12452 (100.0%) missing values Missing
TIME_HUM_INF_MIN has 12452 (100.0%) missing values Missing
TIME_HUM_NORM has 12452 (100.0%) missing values Missing
TIME_HUM_SUP_MIN has 12452 (100.0%) missing values Missing
TIME_HUM_SUP_CRI has 12452 (100.0%) missing values Missing
ID_BATCH_PILE is uniformly distributed Uniform
ID_BATCH_LOT is uniformly distributed Uniform
ID_BATCH_PILE has unique values Unique
CONFORMITY_TYPE is an unsupported type, check if it needs cleaning or further analysis Unsupported
PILE_WEIGHT_BEGIN is an unsupported type, check if it needs cleaning or further analysis Unsupported
LOT_WEIGHT_BEGIN is an unsupported type, check if it needs cleaning or further analysis Unsupported
PILE_WEIGHT_END is an unsupported type, check if it needs cleaning or further analysis Unsupported
LOT_WEIGHT_END is an unsupported type, check if it needs cleaning or further analysis Unsupported
WEIGHT_SUBSTRACT is an unsupported type, check if it needs cleaning or further analysis Unsupported
QR_CODE is an unsupported type, check if it needs cleaning or further analysis Unsupported
QR_CODE_GZ is an unsupported type, check if it needs cleaning or further analysis Unsupported
AVG_TEMPERATURE is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIME_TEMP_INF_CRI is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIME_TEMP_INF_MIN is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIME_TEMP_NORM is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIME_TEMP_SUP_MIN is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIME_TEMP_SUP_CRI is an unsupported type, check if it needs cleaning or further analysis Unsupported
AVG_HUMIDITY is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIME_HUM_INF_CRI is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIME_HUM_INF_MIN is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIME_HUM_NORM is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIME_HUM_SUP_MIN is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIME_HUM_SUP_CRI is an unsupported type, check if it needs cleaning or further analysis Unsupported
NB_TRAY_INOCULATED has 308 (2.5%) zeros Zeros

Reproduction

Analysis started2021-12-29 16:10:42.829500
Analysis finished2021-12-29 16:11:48.032539
Duration1 minute and 5.2 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

ID_BATCH_PILE
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct12452
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size97.4 KiB
P-1098-211102
 
1
P-483-210310
 
1
P-937-210819
 
1
P-2311-211130
 
1
P-771-210812
 
1
Other values (12447)
12447 

Length

Max length16
Median length13
Mean length12.54818503
Min length10

Characters and Unicode

Total characters156250
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12452 ?
Unique (%)100.0%

Sample

1st rowP-1204-210909
2nd rowP-1204-210928
3rd rowP-315-210615
4th rowP-1462-210928
5th rowP-92-210521
ValueCountFrequency (%)
P-1098-2111021
 
< 0.1%
P-483-2103101
 
< 0.1%
P-937-2108191
 
< 0.1%
P-2311-2111301
 
< 0.1%
P-771-2108121
 
< 0.1%
P-328-2101291
 
< 0.1%
P-1250-2107021
 
< 0.1%
P-1612-2112141
 
< 0.1%
P-2385-2110121
 
< 0.1%
P-1033-2110191
 
< 0.1%
Other values (12442)12442
99.9%
2021-12-29T17:11:48.199002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p-2324-2112151
 
< 0.1%
p-435-2104161
 
< 0.1%
p-1052-2111181
 
< 0.1%
p-1469-2111041
 
< 0.1%
p-1015-2107141
 
< 0.1%
p-1113-2107051
 
< 0.1%
p-1609-2108251
 
< 0.1%
p-1288-2112091
 
< 0.1%
p-428-2104011
 
< 0.1%
p-249-2103291
 
< 0.1%
Other values (12442)12442
99.9%

Most occurring characters

ValueCountFrequency (%)
134897
22.3%
225960
16.6%
-24904
15.9%
016894
10.8%
P12452
 
8.0%
96456
 
4.1%
86027
 
3.9%
35906
 
3.8%
65815
 
3.7%
75789
 
3.7%
Other values (7)11150
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number118887
76.1%
Dash Punctuation24904
 
15.9%
Uppercase Letter12459
 
8.0%

Most frequent character per category

ValueCountFrequency (%)
134897
29.4%
225960
21.8%
016894
14.2%
96456
 
5.4%
86027
 
5.1%
35906
 
5.0%
65815
 
4.9%
75789
 
4.9%
55580
 
4.7%
45563
 
4.7%
ValueCountFrequency (%)
P12452
99.9%
N3
 
< 0.1%
U1
 
< 0.1%
K1
 
< 0.1%
O1
 
< 0.1%
W1
 
< 0.1%
ValueCountFrequency (%)
-24904
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common143791
92.0%
Latin12459
 
8.0%

Most frequent character per script

ValueCountFrequency (%)
134897
24.3%
225960
18.1%
-24904
17.3%
016894
11.7%
96456
 
4.5%
86027
 
4.2%
35906
 
4.1%
65815
 
4.0%
75789
 
4.0%
55580
 
3.9%
ValueCountFrequency (%)
P12452
99.9%
N3
 
< 0.1%
U1
 
< 0.1%
K1
 
< 0.1%
O1
 
< 0.1%
W1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII156250
100.0%

Most frequent character per block

ValueCountFrequency (%)
134897
22.3%
225960
16.6%
-24904
15.9%
016894
10.8%
P12452
 
8.0%
96456
 
4.1%
86027
 
3.9%
35906
 
3.8%
65815
 
3.7%
75789
 
3.7%
Other values (7)11150
 
7.1%

ID_BATCH_LOT
Categorical

HIGH CARDINALITY
UNIFORM

Distinct4152
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size97.4 KiB
L-1013-211210
 
6
L-1001-211209
 
6
L-1147-211111
 
4
L-635-210904
 
3
L-776-210731
 
3
Other values (4147)
12430 

Length

Max length13
Median length12
Mean length12.20936396
Min length11

Characters and Unicode

Total characters152031
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowL-698-210909
2nd rowL-698-210928
3rd rowL-162-210615
4th rowL-787-210928
5th rowL-527-210521
ValueCountFrequency (%)
L-1013-2112106
 
< 0.1%
L-1001-2112096
 
< 0.1%
L-1147-2111114
 
< 0.1%
L-635-2109043
 
< 0.1%
L-776-2107313
 
< 0.1%
L-708-2107163
 
< 0.1%
L-1229-2112183
 
< 0.1%
L-1200-2111153
 
< 0.1%
L-1125-2110303
 
< 0.1%
L-538-2105263
 
< 0.1%
Other values (4142)12415
99.7%
2021-12-29T17:11:48.383316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
l-1013-2112106
 
< 0.1%
l-1001-2112096
 
< 0.1%
l-1147-2111114
 
< 0.1%
l-679-2107273
 
< 0.1%
l-1060-2111243
 
< 0.1%
l-1241-2112063
 
< 0.1%
l-966-2109183
 
< 0.1%
l-887-2109063
 
< 0.1%
l-601-2110143
 
< 0.1%
l-604-2106173
 
< 0.1%
Other values (4142)12415
99.7%

Most occurring characters

ValueCountFrequency (%)
132842
21.6%
-24904
16.4%
223643
15.6%
017278
11.4%
L12452
 
8.2%
96394
 
4.2%
66271
 
4.1%
76081
 
4.0%
86050
 
4.0%
55668
 
3.7%
Other values (2)10448
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number114675
75.4%
Dash Punctuation24904
 
16.4%
Uppercase Letter12452
 
8.2%

Most frequent character per category

ValueCountFrequency (%)
132842
28.6%
223643
20.6%
017278
15.1%
96394
 
5.6%
66271
 
5.5%
76081
 
5.3%
86050
 
5.3%
55668
 
4.9%
35230
 
4.6%
45218
 
4.6%
ValueCountFrequency (%)
L12452
100.0%
ValueCountFrequency (%)
-24904
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common139579
91.8%
Latin12452
 
8.2%

Most frequent character per script

ValueCountFrequency (%)
132842
23.5%
-24904
17.8%
223643
16.9%
017278
12.4%
96394
 
4.6%
66271
 
4.5%
76081
 
4.4%
86050
 
4.3%
55668
 
4.1%
35230
 
3.7%
ValueCountFrequency (%)
L12452
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII152031
100.0%

Most frequent character per block

ValueCountFrequency (%)
132842
21.6%
-24904
16.4%
223643
15.6%
017278
11.4%
L12452
 
8.2%
96394
 
4.2%
66271
 
4.1%
76081
 
4.0%
86050
 
4.0%
55668
 
3.7%
Other values (2)10448
 
6.9%

ID_PILE
Categorical

HIGH CARDINALITY

Distinct2848
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Memory size97.4 KiB
351
 
17
788
 
17
786
 
17
660
 
17
658
 
17
Other values (2843)
12367 

Length

Max length7
Median length4
Mean length3.547944105
Min length1

Characters and Unicode

Total characters44179
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique613 ?
Unique (%)4.9%

Sample

1st row1204
2nd row1204
3rd row315
4th row1462
5th row92
ValueCountFrequency (%)
35117
 
0.1%
78817
 
0.1%
78617
 
0.1%
66017
 
0.1%
65817
 
0.1%
50416
 
0.1%
46016
 
0.1%
37016
 
0.1%
72616
 
0.1%
45816
 
0.1%
Other values (2838)12287
98.7%
2021-12-29T17:11:48.567182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78617
 
0.1%
66017
 
0.1%
78817
 
0.1%
35117
 
0.1%
65817
 
0.1%
77316
 
0.1%
37216
 
0.1%
50416
 
0.1%
45816
 
0.1%
37116
 
0.1%
Other values (2838)12287
98.7%

Most occurring characters

ValueCountFrequency (%)
18856
20.0%
26227
14.1%
33868
8.8%
43723
8.4%
73698
8.4%
53688
8.3%
83586
8.1%
63556
8.0%
03495
 
7.9%
93475
 
7.9%
Other values (5)7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number44172
> 99.9%
Uppercase Letter7
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
18856
20.0%
26227
14.1%
33868
8.8%
43723
8.4%
73698
8.4%
53688
8.3%
83586
8.1%
63556
8.1%
03495
 
7.9%
93475
 
7.9%
ValueCountFrequency (%)
N3
42.9%
U1
 
14.3%
K1
 
14.3%
O1
 
14.3%
W1
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common44172
> 99.9%
Latin7
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
18856
20.0%
26227
14.1%
33868
8.8%
43723
8.4%
73698
8.4%
53688
8.3%
83586
8.1%
63556
8.1%
03495
 
7.9%
93475
 
7.9%
ValueCountFrequency (%)
N3
42.9%
U1
 
14.3%
K1
 
14.3%
O1
 
14.3%
W1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII44179
100.0%

Most frequent character per block

ValueCountFrequency (%)
18856
20.0%
26227
14.1%
33868
8.8%
43723
8.4%
73698
8.4%
53688
8.3%
83586
8.1%
63556
8.0%
03495
 
7.9%
93475
 
7.9%
Other values (5)7
 
< 0.1%

ID_LOT
Real number (ℝ≥0)

Distinct1111
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean717.2773852
Minimum58
Maximum1367
Zeros0
Zeros (%)0.0%
Memory size97.4 KiB
2021-12-29T17:11:48.646742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile165
Q1501
median731
Q3959
95-th percentile1200
Maximum1367
Range1309
Interquartile range (IQR)458

Descriptive statistics

Standard deviation313.4473297
Coefficient of variation (CV)0.4369959742
Kurtosis-0.7949196053
Mean717.2773852
Median Absolute Deviation (MAD)229
Skewness-0.1682928773
Sum8931538
Variance98249.22849
MonotocityNot monotonic
2021-12-29T17:11:48.730453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30948
 
0.4%
20548
 
0.4%
16845
 
0.4%
26545
 
0.4%
45042
 
0.3%
49442
 
0.3%
32342
 
0.3%
45439
 
0.3%
31539
 
0.3%
13039
 
0.3%
Other values (1101)12023
96.6%
ValueCountFrequency (%)
583
 
< 0.1%
683
 
< 0.1%
783
 
< 0.1%
846
< 0.1%
859
0.1%
ValueCountFrequency (%)
13673
< 0.1%
13663
< 0.1%
13653
< 0.1%
13643
< 0.1%
13633
< 0.1%

TYPE_PILE_START
Real number (ℝ≥0)

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.16798346
Minimum10
Maximum70
Zeros0
Zeros (%)0.0%
Memory size97.4 KiB
2021-12-29T17:11:48.801955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median20
Q330
95-th percentile30
Maximum70
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.288658913
Coefficient of variation (CV)0.4109810448
Kurtosis-0.453083508
Mean20.16798346
Median Absolute Deviation (MAD)10
Skewness0.1949968991
Sum251131.73
Variance68.70186657
MonotocityNot monotonic
2021-12-29T17:11:48.863828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
204026
32.3%
303864
31.0%
103789
30.4%
12329
 
2.6%
32184
 
1.5%
22146
 
1.2%
30.0151
 
0.4%
20.0117
 
0.1%
5015
 
0.1%
709
 
0.1%
Other values (8)22
 
0.2%
ValueCountFrequency (%)
103789
30.4%
10.061
 
< 0.1%
12329
 
2.6%
12.013
 
< 0.1%
204026
32.3%
ValueCountFrequency (%)
709
 
0.1%
5015
 
0.1%
32184
1.5%
30.073
 
< 0.1%
30.063
 
< 0.1%

TYPE_PILE_END
Real number (ℝ≥0)

MISSING

Distinct63
Distinct (%)0.7%
Missing2931
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean20.34623569
Minimum10
Maximum32
Zeros0
Zeros (%)0.0%
Memory size97.4 KiB
2021-12-29T17:11:48.956624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10.11
Q110.15
median20.12
Q330.2
95-th percentile30.22
Maximum32
Range22
Interquartile range (IQR)20.05

Descriptive statistics

Standard deviation8.131707822
Coefficient of variation (CV)0.3996664516
Kurtosis-1.469313949
Mean20.34623569
Median Absolute Deviation (MAD)9.99
Skewness-0.01910623179
Sum193716.51
Variance66.12467211
MonotocityNot monotonic
2021-12-29T17:11:49.268358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.211277
 
10.3%
10.13687
 
5.5%
30.2665
 
5.3%
10.12553
 
4.4%
20.11537
 
4.3%
20.1526
 
4.2%
10.14483
 
3.9%
30.22468
 
3.8%
20.13465
 
3.7%
30.19411
 
3.3%
Other values (53)3449
27.7%
(Missing)2931
23.5%
ValueCountFrequency (%)
106
< 0.1%
10.033
 
< 0.1%
10.053
 
< 0.1%
10.073
 
< 0.1%
10.0813
0.1%
ValueCountFrequency (%)
3294
0.8%
30.2915
 
0.1%
30.283
 
< 0.1%
30.273
 
< 0.1%
30.2612
 
0.1%

REF_PILE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size97.4 KiB
ILA
4198 
PRE
4122 
IPU
4108 
NC
 
24

Length

Max length3
Median length3
Mean length2.998072599
Min length2

Characters and Unicode

Total characters37332
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRE
2nd rowPRE
3rd rowIPU
4th rowPRE
5th rowPRE
ValueCountFrequency (%)
ILA4198
33.7%
PRE4122
33.1%
IPU4108
33.0%
NC24
 
0.2%
2021-12-29T17:11:49.411743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-12-29T17:11:49.455170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
ila4198
33.7%
pre4122
33.1%
ipu4108
33.0%
nc24
 
0.2%

Most occurring characters

ValueCountFrequency (%)
I8306
22.2%
P8230
22.0%
L4198
11.2%
A4198
11.2%
R4122
11.0%
E4122
11.0%
U4108
11.0%
N24
 
0.1%
C24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter37332
100.0%

Most frequent character per category

ValueCountFrequency (%)
I8306
22.2%
P8230
22.0%
L4198
11.2%
A4198
11.2%
R4122
11.0%
E4122
11.0%
U4108
11.0%
N24
 
0.1%
C24
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin37332
100.0%

Most frequent character per script

ValueCountFrequency (%)
I8306
22.2%
P8230
22.0%
L4198
11.2%
A4198
11.2%
R4122
11.0%
E4122
11.0%
U4108
11.0%
N24
 
0.1%
C24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII37332
100.0%

Most frequent character per block

ValueCountFrequency (%)
I8306
22.2%
P8230
22.0%
L4198
11.2%
A4198
11.2%
R4122
11.0%
E4122
11.0%
U4108
11.0%
N24
 
0.1%
C24
 
0.1%

NB_TRAY_INOCULATED
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)0.1%
Missing474
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean4.877692436
Minimum0
Maximum6
Zeros308
Zeros (%)2.5%
Memory size97.4 KiB
2021-12-29T17:11:49.514884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.331496518
Coefficient of variation (CV)0.2729767273
Kurtosis3.356931317
Mean4.877692436
Median Absolute Deviation (MAD)1
Skewness-1.754275216
Sum58425
Variance1.772882977
MonotocityNot monotonic
2021-12-29T17:11:49.568076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
64513
36.2%
54420
35.5%
41614
 
13.0%
3647
 
5.2%
2374
 
3.0%
0308
 
2.5%
1102
 
0.8%
(Missing)474
 
3.8%
ValueCountFrequency (%)
0308
 
2.5%
1102
 
0.8%
2374
 
3.0%
3647
5.2%
41614
13.0%
ValueCountFrequency (%)
64513
36.2%
54420
35.5%
41614
 
13.0%
3647
 
5.2%
2374
 
3.0%

DATE_BEGIN
Date

Distinct7644
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Memory size97.4 KiB
Minimum2021-01-04 09:32:09.623000
Maximum2021-12-24 08:14:36.327000
2021-12-29T17:11:49.639972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:49.722559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DATE_INJECTION
Date

Distinct6866
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Memory size97.4 KiB
Minimum2021-01-04 09:32:09.623000
Maximum2021-12-24 08:14:36.330000
2021-12-29T17:11:49.815454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:49.897811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DATE_INOCULATION
Date

MISSING

Distinct4754
Distinct (%)40.7%
Missing765
Missing (%)6.1%
Memory size97.4 KiB
Minimum2021-02-05 15:05:00
Maximum2021-12-23 18:49:26.417000
2021-12-29T17:11:49.990815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:50.063492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DATE_END_CYCLE
Date

MISSING

Distinct7406
Distinct (%)67.8%
Missing1528
Missing (%)12.3%
Memory size97.4 KiB
Minimum2021-01-01 00:01:00
Maximum2021-12-24 08:12:37.767000
2021-12-29T17:11:50.153664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:50.235435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DATE_SIEVING
Date

MISSING

Distinct6788
Distinct (%)68.4%
Missing2523
Missing (%)20.3%
Memory size97.4 KiB
Minimum2021-01-11 13:17:46.790000
Maximum2021-12-24 08:12:37.767000
2021-12-29T17:11:50.327765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:50.401551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DATE_END
Date

MISSING

Distinct5748
Distinct (%)59.9%
Missing2848
Missing (%)22.9%
Memory size97.4 KiB
Minimum2021-01-11 13:17:46.790000
Maximum2021-12-24 08:14:36.327000
2021-12-29T17:11:50.493466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:50.573125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

IS_CONFORM
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing1560
Missing (%)12.5%
Memory size97.4 KiB
True
8723 
False
2169 
(Missing)
1560 
ValueCountFrequency (%)
True8723
70.1%
False2169
 
17.4%
(Missing)1560
 
12.5%
2021-12-29T17:11:50.636685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

CONFORMITY_TYPE
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

AGE_PILE
Real number (ℝ≥0)

MISSING

Distinct46
Distinct (%)0.4%
Missing765
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean15.33310516
Minimum0
Maximum317
Zeros25
Zeros (%)0.2%
Memory size97.4 KiB
2021-12-29T17:11:50.688353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q111
median14
Q320
95-th percentile22
Maximum317
Range317
Interquartile range (IQR)9

Descriptive statistics

Standard deviation15.76286552
Coefficient of variation (CV)1.028028266
Kurtosis288.6377443
Mean15.33310516
Median Absolute Deviation (MAD)4
Skewness15.97350416
Sum179198
Variance248.4679293
MonotocityNot monotonic
2021-12-29T17:11:50.760338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
211472
11.8%
131305
10.5%
121111
8.9%
111062
8.5%
14971
 
7.8%
10968
 
7.8%
20768
 
6.2%
15574
 
4.6%
22513
 
4.1%
9409
 
3.3%
Other values (36)2534
20.4%
(Missing)765
 
6.1%
ValueCountFrequency (%)
025
 
0.2%
1108
0.9%
296
0.8%
396
0.8%
4123
1.0%
ValueCountFrequency (%)
3173
< 0.1%
3166
< 0.1%
3123
< 0.1%
3103
< 0.1%
3083
< 0.1%

PILE_POSITION
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size97.4 KiB
2.0
3687 
1.0
3687 
3.0
3686 
nan
1392 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37356
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row3.0
ValueCountFrequency (%)
2.03687
29.6%
1.03687
29.6%
3.03686
29.6%
nan1392
 
11.2%
2021-12-29T17:11:50.903234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-12-29T17:11:50.954195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.03687
29.6%
1.03687
29.6%
3.03686
29.6%
nan1392
 
11.2%

Most occurring characters

ValueCountFrequency (%)
.11060
29.6%
011060
29.6%
23687
 
9.9%
13687
 
9.9%
33686
 
9.9%
n2784
 
7.5%
a1392
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22120
59.2%
Other Punctuation11060
29.6%
Lowercase Letter4176
 
11.2%

Most frequent character per category

ValueCountFrequency (%)
011060
50.0%
23687
 
16.7%
13687
 
16.7%
33686
 
16.7%
ValueCountFrequency (%)
n2784
66.7%
a1392
33.3%
ValueCountFrequency (%)
.11060
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common33180
88.8%
Latin4176
 
11.2%

Most frequent character per script

ValueCountFrequency (%)
.11060
33.3%
011060
33.3%
23687
 
11.1%
13687
 
11.1%
33686
 
11.1%
ValueCountFrequency (%)
n2784
66.7%
a1392
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII37356
100.0%

Most frequent character per block

ValueCountFrequency (%)
.11060
29.6%
011060
29.6%
23687
 
9.9%
13687
 
9.9%
33686
 
9.9%
n2784
 
7.5%
a1392
 
3.7%

PILE_WEIGHT_BEGIN
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

LOT_WEIGHT_BEGIN
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

PILE_WEIGHT_END
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

LOT_WEIGHT_END
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

WEIGHT_SUBSTRACT
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

QR_CODE
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

QR_CODE_GZ
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

AVG_TEMPERATURE
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

TIME_TEMP_INF_CRI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

TIME_TEMP_INF_MIN
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

TIME_TEMP_NORM
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

TIME_TEMP_SUP_MIN
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

TIME_TEMP_SUP_CRI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

AVG_HUMIDITY
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

TIME_HUM_INF_CRI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

TIME_HUM_INF_MIN
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

TIME_HUM_NORM
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

TIME_HUM_SUP_MIN
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

TIME_HUM_SUP_CRI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing12452
Missing (%)100.0%
Memory size97.4 KiB

Interactions

2021-12-29T17:10:47.123778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:10:58.063663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:07.319453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:18.500627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:28.380033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:32.881785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:33.039009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:33.119209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:33.210946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:36.515188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:36.679638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:36.833640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:36.999187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:41.468709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:41.559464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:41.692170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:41.786157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:45.910764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:46.002434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-29T17:11:46.147597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-12-29T17:11:51.005314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-29T17:11:51.087040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-29T17:11:51.168827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-29T17:11:51.270840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-12-29T17:11:51.370775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-12-29T17:11:46.366028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-29T17:11:47.380234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-29T17:11:47.656449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-29T17:11:47.851269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ID_BATCH_PILEID_BATCH_LOTID_PILEID_LOTTYPE_PILE_STARTTYPE_PILE_ENDREF_PILENB_TRAY_INOCULATEDDATE_BEGINDATE_INJECTIONDATE_INOCULATIONDATE_END_CYCLEDATE_SIEVINGDATE_ENDIS_CONFORMCONFORMITY_TYPEAGE_PILEPILE_POSITIONPILE_WEIGHT_BEGINLOT_WEIGHT_BEGINPILE_WEIGHT_ENDLOT_WEIGHT_ENDWEIGHT_SUBSTRACTQR_CODEQR_CODE_GZAVG_TEMPERATURETIME_TEMP_INF_CRITIME_TEMP_INF_MINTIME_TEMP_NORMTIME_TEMP_SUP_MINTIME_TEMP_SUP_CRIAVG_HUMIDITYTIME_HUM_INF_CRITIME_HUM_INF_MINTIME_HUM_NORMTIME_HUM_SUP_MINTIME_HUM_SUP_CRI
0P-1204-210909L-698-210909120469812.0010.13PRE5.02021-09-09 20:00:36.5972021-09-09 20:00:36.6002021-09-10 17:30:00.0002021-09-23 19:07:17.6232021-09-23 19:07:17.6232021-09-28 18:43:03.180TrueNone13.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
1P-1204-210928L-698-210928120469810.0010.11PRE5.02021-09-28 18:43:03.1802021-09-28 18:43:03.1802021-09-28 18:45:00.0002021-10-09 07:26:43.6832021-10-09 07:26:43.6832021-10-11 17:52:26.663TrueNone11.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
2P-315-210615L-162-21061531516232.0032.00IPUNaN2021-06-15 14:05:01.1632021-06-15 14:05:01.163NaT2021-06-15 19:40:37.030NaTNaTFalseNoneNaN1.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
3P-1462-210928L-787-210928146278712.0110.15PRE5.02021-09-28 10:48:03.9272021-09-28 10:48:03.9272021-10-01 06:25:00.0002021-10-16 06:45:22.9002021-10-16 06:45:22.9002021-10-16 14:54:14.467TrueNone15.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
4P-92-210521L-527-2105219252710.0010.14PRE5.02021-05-21 11:17:38.9732021-05-21 11:17:38.9732021-05-21 11:25:00.0002021-06-04 13:00:23.3732021-06-04 13:00:23.3732021-06-04 15:42:19.930TrueNone14.03.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
5P-1294-211118L-718-211118129471820.0020.13ILA6.02021-11-18 19:38:39.2132021-11-18 19:38:39.2132021-11-18 19:38:39.2132021-12-01 06:41:57.5432021-12-01 06:41:57.5432021-12-02 14:38:15.403TrueNone13.01.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
6P-1462-210826L-787-210826146278710.0010.14PRE4.02021-08-26 14:57:52.2872021-08-26 14:57:52.2872021-08-26 16:30:00.0002021-09-09 07:18:16.4502021-09-09 07:18:16.4502021-09-09 14:01:53.333TrueNone14.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
7P-730-210513L-510-21051373051030.0030.22IPU5.02021-05-13 13:15:22.1272021-05-13 13:15:22.1302021-05-13 13:20:00.0002021-06-04 08:00:58.5132021-06-04 08:00:58.5132021-06-04 08:01:20.267TrueNone22.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
8P-730-210604L-510-21060473051010.0010.13PRE5.02021-06-04 08:01:20.2672021-06-04 08:01:20.2672021-06-04 09:55:00.0002021-06-17 06:02:04.1472021-06-17 06:02:04.1472021-12-01 19:15:41.447TrueNone13.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
9P-1439-210713L-760-210713143976030.0030.20IPU6.02021-07-13 15:01:53.9972021-07-13 15:01:53.9972021-07-13 15:01:53.9972021-08-02 05:34:49.0372021-08-02 05:34:49.0372021-08-04 12:14:56.567TrueNone20.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone

Last rows

ID_BATCH_PILEID_BATCH_LOTID_PILEID_LOTTYPE_PILE_STARTTYPE_PILE_ENDREF_PILENB_TRAY_INOCULATEDDATE_BEGINDATE_INJECTIONDATE_INOCULATIONDATE_END_CYCLEDATE_SIEVINGDATE_ENDIS_CONFORMCONFORMITY_TYPEAGE_PILEPILE_POSITIONPILE_WEIGHT_BEGINLOT_WEIGHT_BEGINPILE_WEIGHT_ENDLOT_WEIGHT_ENDWEIGHT_SUBSTRACTQR_CODEQR_CODE_GZAVG_TEMPERATURETIME_TEMP_INF_CRITIME_TEMP_INF_MINTIME_TEMP_NORMTIME_TEMP_SUP_MINTIME_TEMP_SUP_CRIAVG_HUMIDITYTIME_HUM_INF_CRITIME_HUM_INF_MINTIME_HUM_NORMTIME_HUM_SUP_MINTIME_HUM_SUP_CRI
12442P-2007-211015L-1024-2110152007102420.0020.11ILA0.02021-10-15 14:46:34.3402021-10-15 14:46:34.3402021-10-15 15:15:00.0002021-10-26 12:05:03.4372021-10-26 12:05:03.4372021-10-27 18:44:50.400TrueNone11.01.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
12443P-1145-210618L-630-210618114563030.0030.20IPU5.02021-06-18 16:21:51.1632021-06-18 16:21:51.1672021-06-18 16:21:51.1672021-07-08 07:13:35.9502021-07-08 07:13:35.9502021-08-12 17:35:28.410TrueNone20.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
12444P-1327-211108L-733-211108132773320.0020.11ILA3.02021-11-08 18:19:24.7802021-11-08 18:19:24.7802021-11-08 18:19:24.7802021-11-19 05:53:02.5502021-11-19 05:53:02.5502021-11-24 21:32:56.933TrueNone11.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
12445P-1981-210924L-993-210924198199310.0010.14PRE4.02021-09-24 15:41:54.9102021-09-24 15:41:54.9132021-09-24 15:41:54.9132021-10-08 07:18:32.7672021-10-08 07:18:32.7672021-10-18 17:16:03.217TrueNone14.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
12446P-625-210916L-258-21091662525812.0010.13PRE4.02021-09-16 13:03:30.8172021-09-16 13:03:30.8172021-09-16 13:03:30.8172021-09-29 08:31:58.1932021-09-29 08:31:58.1932021-09-29 09:01:29.950TrueNone13.03.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
12447P-1505-210817L-891-210817150589110.0010.11PRE4.02021-08-17 13:53:31.6472021-08-17 13:53:31.6502021-08-17 13:53:31.6502021-08-28 11:52:55.4202021-08-28 11:52:55.4202021-08-28 14:45:25.583TrueNone11.03.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
12448P-1874-211001L-950-211001187495010.0010.15PRE5.02021-10-01 09:33:26.7202021-10-01 09:33:26.7232021-10-01 09:33:26.7232021-10-16 07:18:03.9272021-10-16 07:18:03.9272021-10-21 11:39:12.663TrueNone15.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
12449P-1156-210825L-687-210825115668730.0030.22IPU6.02021-08-25 10:06:17.8632021-08-25 10:06:17.8672021-08-25 10:06:17.8672021-09-16 07:26:37.7972021-09-16 07:26:37.7972021-09-16 14:11:49.997TrueNone22.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
12450P-2743-211220L-1332-2112202743133220.00NoneILANaN2021-12-20 18:56:01.9372021-12-20 18:56:01.937NaTNaTNaTNaTNoneNoneNaN1.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
12451821P-814-210618L-323-21061882132312.0020.15PRE5.02021-06-18 11:51:19.3432021-06-18 11:51:19.3432021-06-18 12:25:00.0002021-07-08 12:45:19.6502021-07-08 12:45:19.6502021-07-08 13:14:37.507TrueNone20.02.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone